DDLA: dual deep learning architecture for classification of plant species

Abstract

Plant species recognition is performed using a dual deep learning architecture (DDLA) approach. DDLA consists of MobileNet and DenseNet-121 architectures. The feature vectors obtained from individual architectures are concatenated to form a final feature vector. The extracted features are then classified using machine learning (ML) classifiers such as linear discriminant analysis, multinomial logistic regression (LR), Naive Bayes, classification and regression tree, k -nearest neighbour, random forest classifier, bagging classifier and multi-layer perceptron. The dataset considered in the studies is standard (Flavia, Folio, and Swedish Leaf) and custom collected (Leaf-12) dataset. The MobileNet and DenseNet-121 architectures are also used as a feature extractor and a classifier. It is observed that the DDLA architecture with LR classifier produced the highest accuracies of 98.71, 96.38, 99.41, and 99.39% for Flavia, Folio, Swedish leaf, and Leaf-12 datasets. The observed accuracy for DDLA + LR is higher compared with other approaches (DDLA + ML classifiers, MobileNet + ML classifiers, DenseNet-121 + ML classifiers, MobileNet + fully connected layer (FCL), DenseNet-121 + FCL). It is also observed that the DDLA architecture with LR classifier achieves higher accuracy in comparable computation time with other approaches.